Consider the example dialogs in Figures REF  and REF .
In Figure REF , the Commander simply tells the Follower to prepare coffee, but in Figure REF , the Commander provides much lower level instructions, and waits for the Follower to complete each step. The initial instruction provided in Figure REF  (“can you make me a coffee please?”) are similar to goal-level instructions and requests typically seen in task-oriented dialog.
The instructions in Figure REF  (“grab the dirty mug out of the fridge, go wash in the sink) are more similar to the detailed instructions in datasets such as R2R [1]}. Every trajectory in ALFRED [2]} is annotated with instructions at both of these levels of granularity.
In TEACh, by contrast, dialogues may contain either one or in some cases both levels of granularity.
Thus, TEACh benchmark agents need to be able to effectively map instructions at different levels of granularity to low level actions.
